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Key Features
- Analyse data with ready-to-use and customizable recipes
- Discover convenient functions to speed-up your work and data files
- Explore the leading R packages built for expert data analysis
Data analysis has recently emerged as a very important focus for a huge range of organizations and businesses. R makes detailed data analysis easier, making advanced data exploration and insight accessible to anyone interested in learning it.
This book empowers you by showing you ways to use R to generate professional analysis reports. It provides examples for various important analysis and machine-learning tasks that you can try out with associated and readily available data. The book also teaches you to quickly adapt the example code for your own needs and save yourself the time needed to construct code from scratch.
What you will learn- Get data into your R environment and prepare it for analysis
- Perform exploratory data analyses and generate meaningful visualizations of the data
- Apply several machine-learning techniques for classification and regression
- Get your hands around large data sets with the help of reduction techniques
- Extract patterns from time-series data and produce forecasts based on them
- Learn how to extract actionable information from social network data
- Implement geospatial analysis
- Present your analysis convincingly through reports and build an infrastructure to enable others to play with your data
Viswa Viswanathan is an associate professor of Computing and Decision Sciences at the Stillman School of Business in Seton Hall University. After completing his PhD in artificial intelligence, Viswa spent a decade in academia and then switched to a leadership position in the software industry for a decade.
Shanthi Viswanathan is an experienced technologist who has delivered technology management and enterprise architecture consulting to many enterprise customers. She has worked for Infosys Technologies, Oracle Corporation, and Accenture. As a consultant, Shanthi has helped several large organizations, such as Canon, Cisco, Celgene, Amway, Time Warner Cable, and GE among others, in areas such as data architecture and analytics, master data management, service-oriented architecture, business process management, and modeling.
Table of Contents- Sales Rank: #866971 in eBooks
- Published on: 2015-05-27
- Released on: 2015-05-27
- Format: Kindle eBook
About the Author
Viswa Viswanathan
Viswa Viswanathan is an associate professor of Computing and Decision Sciences at the Stillman School of Business in Seton Hall University. After completing his PhD in artificial intelligence, Viswa spent a decade in academia and then switched to a leadership position in the software industry for a decade. During this period, he worked for Infosys, Igate, and Starbase. He embraced academia once again in 2001. Viswa has taught extensively in fields ranging from operations research, computer science, software engineering, management information systems, and enterprise systems. In addition to teaching at the university, Viswa has conducted training programs for industry professionals. He has written several peer-reviewed research publications in journals such as Operations Research, IEEE Software, Computers and Industrial Engineering, and International Journal of Artificial Intelligence in Education. He has authored a book titled Data Analytics with R: A hands-on approach. Viswa thoroughly enjoys hands-on software development, and has single-handedly conceived, architected, developed, and deployed several web-based applications. Apart from his deep interest in technical fields such as data analytics, artificial intelligence, computer science, and software engineering, Viswa harbors a deep interest in education, with special emphasis on the roots of learning and methods to foster deeper learning. He has done research in this area and hopes to pursue the subject further. Viswa would like to express deep gratitude to professors Amitava Bagchi and Anup Sen, who were inspirational forces during his early research career. He is also grateful to several extremely intelligent colleagues, notable among them being Rajesh Venkatesh, Dan Richner, and Sriram Bala, who significantly shaped his thinking. His aunt, Analdavalli; his sister, Sankari; and his wife, Shanthi, taught him much about hard work, and even the little he has absorbed has helped him immensely. His sons, Nitin and Siddarth, have helped with numerous insightful comments on various topics.
Shanthi Viswanathan
Shanthi Viswanathan is an experienced technologist who has delivered technology management and enterprise architecture consulting to many enterprise customers. She has worked for Infosys Technologies, Oracle Corporation, and Accenture. As a consultant, Shanthi has helped several large organizations, such as Canon, Cisco, Celgene, Amway, Time Warner Cable, and GE among others, in areas such as data architecture and analytics, master data management, service-oriented architecture, business process management, and modeling. When she is not in front of her Mac, Shanthi spends time hiking in the suburbs of NY/NJ, working in the garden, and teaching yoga. Shanthi would like to thank her husband, Viswa, for all the great discussions on numerous topics during their hikes together and for exposing her to R and Java. She would also like to thank her sons, Nitin and Siddarth, for getting her into the data analytics world.
Most helpful customer reviews
2 of 2 people found the following review helpful.
There are better options
By Dimitri Shvorob
Packt's Indian roots and willingness to publish anything, on one hand, and India's vast supply of English-speaking IT specialists, on the other hand, have been a dangerous combination: burned by several disasters in book form, I was opening "R Data Analysis Cookbook" with some trepidation. In the event, I was pleasantly surprised to discover a below-average-quality but decent book, which supplements familiar "standards", done by other books, with original material, e.g. when it discusses calling R from Java, or uses RgoogleMaps package. I would guess a 50/50 split of the page count between (a) machine-learning/"data science" methods and (b) "everything else", primarily data manipulation and visualization. My concern is that the latter is not sufficiently substantial to make up for the fact that on the former, "R Data Analysis Cookbook" is weak, whether viewed in isolation or compared against other titles. I would not recommend the purchase, and suggest "R in Action" by Kabacoff - a high-quality R introduction with a substantial statistics-and-machine-learning component - of the heftier and more expensive "R Book" by Crowley as superior alternatives.
UPD. With the benefit of a little more life experience, I would say: don't spend your time on *any* R book. Python is the way to go.
0 of 0 people found the following review helpful.
good R book with succinct code and clear explanations
By DWR
The R Data Analysis Cookbook offers succinct examples on a variety of data analysis topics including vectorized operations, using the apply / lapply / sapply family of functions, exploratory data analysis, classification, regression, data reduction techniques and geospatial analysis. I enjoyed the sections that dealt with using shiny and the various treatments of regression techniques. I thought the section that covered time series analysis was well done overall, and would have liked to have seen the authors expand upon that topic in more detail, though I understand that space must have been somewhat constrained given the number of topics this volume addresses.
1 of 1 people found the following review helpful.
The book provides recipes relevant to start your data analysis side project using R.
By Venkatesh-Prasad Ranganath
Of the various books about R and data analysis, this one hits the road running. It is primarily focused on data analysis using R (in that order). To do so, it assumes that you know what you want to get done with data analysis and provides recipes to get you started with various tasks to do data analysis.
That said, it does not provide information about R language features or explain how a specific analysis technique works. For such information, you will have to look elsewhere such as "Art of R Programming" and "Machine Learning: An Algorithmic Perspective".
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